Discrete hopfield neural network in restricted maximum k-Satisfiability logic programming

Abstract

Maximum k-Satisfiability (MAX-kSAT) consists of the most consistent interpretation that generate the maximum number of satisfied clauses. MAX-kSAT is an important logic representation in logic programming since not all combinatorial problem is satisfiable in nature. This paper presents Hopfield Neural Network based on MAX-kSAT logical rule. Learning of Hopfield Neural Network will be integrated with Wan Abdullah method and Sathasivam relaxation method to obtain the correct final state of the neurons. The computer simulation shows that MAX-kSAT can be embedded optimally in Hopfield Neural Network.